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Academic Integrity Rewrite

Community
lin1111-1
academic-integrity-rewrite

Revise Chinese or English academic writing for lower unnecessary textual overlap while preserving meaning, evidence, numbers, equations, terminology, and citations. Use for 学术论文降重、查重后修改、重复段落重写、paraphrasing, similarity-report remediation, abstracts, literature reviews, methods, results, discussions, theses, and manuscripts in DOCX, Markdown, or plain text. Do not use to disguise plagiarism, fabricate sources, or evade academic-integrity review.

Overview

Publisherlin1111-1
Repositoryacademic-integrity-rewrite
Skill nameacademic-integrity-rewrite
Stars
102
Forks
3
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lin1111-1 on GitHub. Read the source before you install it.

Installation

Install the Academic Integrity Rewrite AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lin1111-1/academic-integrity-rewrite/tree/main/skills/academic-integrity-rewrite
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lin1111-1/academic-integrity-rewrite.git /tmp/academic-integrity-rewrite
mkdir -p .claude/skills
cp -r /tmp/academic-integrity-rewrite/skills/academic-integrity-rewrite .claude/skills/academic-integrity-rewrite
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Integrity Rewrite in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Academic Integrity Rewrite on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Academic Integrity Rewrite is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Academic Integrity Rewrite

Rewrite from the research logic and evidence, not by replacing words sentence by sentence. Treat originality as clearer authorship and synthesis, never as detector gaming.

Establish the task

  1. Identify the target language, field, venue or style guide, editable sections, and any similarity report.
  2. Distinguish the author's findings from cited ideas, standard definitions, methods that require precise wording, and text that must remain verbatim.
  3. If the source or citation evidence is unavailable, mark claims for verification instead of inventing details.
  4. State that no exact similarity score can be guaranteed because databases and algorithms differ.

Triage before rewriting

Classify each flagged passage:

  • P0 — integrity or technical risk: missing attribution, changed data, equations, labels, units, citations, or unsupported claims. Resolve first.
  • P1 — reasoning risk: source-by-source summary, weak synthesis, duplicated logic, overclaiming, or a mechanism not supported by the model or data.
  • P2 — expression risk: formulaic phrasing, noun stacking, repeated sentence frames, vague subjects, or unnecessary metadiscourse.
  • P3 — presentation risk: inconsistent terms, symbols, figure/table references, citation format, or typography.

Read references/rewrite-methods.md for section-specific strategies. Read references/quality-gates.md before final review.

Build a protected fact ledger

Record, before drafting:

  • every number, sign, range, unit, symbol, equation, boundary condition, and named method;
  • every citation marker and the claim it supports;
  • approved terminology, abbreviations, figure/table/equation numbers, and causal qualifiers;
  • direct quotations or legally/academically fixed language that must not be paraphrased.

Never silently change an item in this ledger. Flag contradictions or likely source errors for the author.

Reconstruct the passage

  1. Reduce the passage to an evidence map: purpose → method/evidence → result → interpretation → limitation.
  2. Close the source text where practical and draft from that map.
  3. Reorder claims by logic, combine redundant sentences, split overloaded sentences, and make the actor or evidence explicit.
  4. Synthesize multiple sources by theme, agreement, disagreement, method, chronology, or research gap. Preserve attribution at claim level.
  5. Prefer precise verbs and quantified statements. Remove empty frames such as “it is obvious,” “it can be seen,” and repeated announcements of what follows.
  6. Preserve necessary technical terms. Do not force synonyms for established concepts.

Validate

Run the deterministic audit when both original and revision are available:

bash
python scripts/audit_revision.py ORIGINAL REVISED

Use --json PATH for a machine-readable report. It compares numbers, common unit-bearing measurements, and citation markers. Treat its warnings as review prompts, not proof of plagiarism or correctness; uncommon or field-specific unit notation still requires manual review.

Then verify manually:

  1. Compare the protected fact ledger against the revision.
  2. Check every citation against its supported claim and, when possible, the primary source.
  3. Check equations, terminology, abbreviations, units, and cross-references globally.
  4. Confirm that interpretations do not exceed the method, model, or data.
  5. Read the revision for natural academic flow and field-appropriate tone.

Deliver

Return:

  • the revised passage;
  • a concise change rationale grouped by logic, evidence, and expression;
  • a verification list for any unresolved facts, citations, or labels;
  • an audit summary stating whether numbers and citation markers were preserved and which long shared spans remain.

Do not claim guaranteed acceptance, guaranteed originality, or a guaranteed similarity percentage.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Academic Integrity Rewrite AI skill do?

Revise Chinese or English academic writing for lower unnecessary textual overlap while preserving meaning, evidence, numbers, equations, terminology, and citations. Use for 学术论文降重、查重后修改、重复段落重写、paraphrasing, similarity-report remediation, abstracts, literature reviews, methods, results, discussions, theses, and manuscripts in DOCX, Markdown, or plain text. Do not use to disguise plagiarism, fabricate sources, or evade academic-integrity review.

Why use Academic Integrity Rewrite on TypingMind?

Because you install it once and use it with any model. Academic Integrity Rewrite is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Academic Integrity Rewrite in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lin1111-1/academic-integrity-rewrite/tree/main/skills/academic-integrity-rewrite. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Academic Integrity Rewrite?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Academic Integrity Rewrite?

As many as you like. As long as a model supports skills, you can use Academic Integrity Rewrite with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Academic Integrity Rewrite AI skill free?

Yes. It is published on GitHub by lin1111-1 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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